Pre-installed Computer Vision Software
Contains the OpenVINO(TM) toolkit for hardware acceleration of deep learning inference for computer vision applications.
Hardware Acceleration
Harness the performance of Intel®-based accelerators for deep learning inference with the CPU, GPU, and VPU included in this kit.
Reduce Time to Field Trial
The kit includes a field-ready mountable aluminum enclosure and camera. Purchase available Wi-Fi and cellular module add-ons.
Benefits of the OpenVINO™ toolkit
Accelerate Performance
Access Intel computer vision accelerators. Speed code performance.
Supports heterogeneous processing & Asynchronous execution.
Integrate Deep learning
Unleash convolutional neural network (CNN) based deep learning inference across using a common API & 10 trained models.
Speed development
Reduce time using a library of optimized OpenCV* & OpenVX* functions, 15+ samples.
Develop once, deploy for current & future Intel-based devices.
Innovate & customize
Use the increasing repository of OpenCL™ starting points in OpenCV* to add your own unique code.
UP Squared AI Vision X Developer kit
| What's in the box |
| A VESA Mountable edge computer with Intel Atom®X7-E3950 processor, 8GB memory, 64 GB eMMC with Ubuntu image (kernel 4.15) OpenVINO™ toolkit R5 |
| Power supply 5V @ 6A |
| EU/US Power cord |
| AI Core X integrated with Intel® Movidius™ Myriad™ X (version B) |
| USB Camera with maximum resolution of 1920 x 1080p at 30 fps |
| Software |
| Ubuntu 16.04 Desktop |
| OpenVINO™ toolkit R5 |
| Intel® System Studio 2018 Community Edition with Eclipse IDE |
| Drivers for Intel® VTune™ Amplifier, Intel® Energy Profiler, Intel® Graphics Performance Analyzer |
| MRAA and UPM I/O and sensor libraries for C++, Python, Java and JavaScript |
USB Vision camera
| Maximum resolution | Full HD 1920 x 1080 |
| Supported video quality | 320 x 240 / 352 x 288 / 640 x 480 VGA / 800 x 600 MJPEG @ 60 fps 1024 x 768 / 1280 x 720 / 1280 x 1024 / 1920 x 1080 MJPEG @ 30 fps |
| Picture format | MJPEG / YUV2 (YUYV) |
| Focus motor | Manual focus |
| Sensor | 1/2.7" OV2735 |
| Mini illumination | 0.05 LUX |
| Interface | USB 2.0 High Speed |
| Lens | F3.6mm |
| Support OTG | USB 2.0 OTG |
| Support free driver | USB Video Class (UVC) |
| Dimensions | 41 x 41 x 41.5 mm / 245g |
| Exposure | Auto |
| Auto white balance | Auto |
| AGC | Support |
| Working temperature | -20°C to 80°C |
| Operating humidity | 30%~90% Rh |
| Working power | DC 5V |
| Cable length | 1 meter |
AI Core X Specifications
| Soc | Intel® Movidius™ Myriad™ X VPU 2485 X |
| Supported Frameworks | TensorFlow, Caffe |
| Form factor | Mini PCI-Express |
| Dimensions | 51 x 30 mm |
| Minimum system requirements | x86_64 Computer running Ubuntu 16.04 |
| Available mPCI-E slot | |
| 1GB RAM | |
| 4GB Free storage space |
UP Squared Specifications
| Soc | Intel® Atom™ x7-E3950 (up to 2GHz) |
| Graphics | Intel® Gen 9 HD |
| Video and Audio | 1x HDMI 1.4b 4K@30Hz, 1x DP 1.2 4K @ 60Hz |
| Camera interface | CSI 2-lane + CSI 4-lane (only pass-through) |
| Memory | 8GB |
| Storage | 64GB |
| USB | 3x USB3.0 (Type A) 1x USB 3.0 OTG (micro B) 2x USB2.0 + 2x UART (Tx/Rx) |
| Ethernet | 2x Gb Ethernet full speed RJ-45 |
| RTC | Yes |
| Expansion | 40 pin General Purpose bus 60 pin EXHAT 1x mini-PCI-e, M.2 2230, SATA3 |
| OS Support | MS Windows 10 (full) MS Windows IoT Core Linux (ubilinux, Ubuntu, Yocto) Android Marshmallow |
| Dimensions | 85.6 x 90 mm |
Expand your UP Squared board with a wide range of connectivity expansions
